基于GWO-SVM算法的中国车辆车牌识别研究

Hao Ding, Jia–qi Shen
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引用次数: 0

摘要

. 车牌识别技术在交通管理系统中得到了广泛的应用。为了提高传统LPR的效率,本文提出了一种基于支持向量机(SVM)模型和灰狼优化(GWO)算法的轻量级LPR算法。采用GWO算法寻求支持向量机惩罚因子和核参数的最优参数,提高了车牌字符识别的准确率。此外,为了提高灰度车牌图像的质量,还引入了高斯滤波和灰度拉伸技术进行图像预处理。实验结果表明,所提出的字符识别模型的识别准确率可以达到95%以上。与目前使用支持向量机的LPR模型相比,该算法迭代速度快得多。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on License Plate Recognition of Chinese Vehicle Based on GWO-SVM Algorithm
. License Plate Recognition (LPR) technology has been widely used in traffic management system. In order to improve the efficiency of traditional LPR, this paper proposes a lightweight LPR algorithm based on Support Vector Machine (SVM) model with Grey Wolf Optimization (GWO) algorithm. GWO algorithm is used to seek the optimal parameters of the penalty factor and kernel parameter of SVM, which improves the accuracy of license plate character recognition. Besides, Gaussian filtering and grey level stretching are introduced for image preprocessing to enhance the quality of the gray level license plate image. Experiment results show that the recognition accuracy of the proposed character recognition model can reach more than 95%. Compared with state-of-the-art LPR models using SVM, this algorithm is much faster on iteration.
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